TRIPODS+X:RES:Collaborative Research: Learning with Expert-In-The-Loop for Multimodal Weakly Labeled Data and an Application to Massive Scale Medical Imaging

在这个项目中开发的方法解决了机器学习和生命科学应用界面上的紧迫问题。这项工作希望刺激丰富多样的后续研究，不仅建立在开发的数学工具上，而且还建立在项目对可解释和交互式机器学习的关注中获得的见解上。作为一个关键的应用，从这项研究中获得的技术在医学图像上的实施将实现自动报告生成。这项技术可以大大减少放射科医生的工作量，从而减少人为错误，优化资源利用。最终，减少放射学中的人为错误或遗漏病例可以极大地有利于患者的健康和护理。更广泛地说，这项工作将加强医疗保健中数据驱动思维的持续采用，从而有助于加速新发现。这个项目也影响到教育，涉及到学生在不同学术阶段的智力和专业发展。机器学习在医学成像（以及其他以人为中心的任务）中的大多数应用都集中在监督学习上，这需要大量昂贵的标记数据。这种限制在整个应用程序中反复出现，而机器学习的实际使用需要鲁棒性以及在有限监督下工作的能力。该项目侧重于开发用于弱监督的新机器学习工具，同时推进可解释和交互式学习的最新技术。此外，在可行的情况下，关注大规模数据和领域知识的结合。该计划亦应将理论进展应用于实际的医学影像应用。所提出的工作的理论进展将依赖于几何工具，特别是度量学习（包括无限维空间），由最优传输理论驱动的数学模型，以及基于神经网络和核方法的非线性表示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The methods developed in this project address pressing problems at the interface of machine learning and a life-sciences application. This work hopes to spur a rich variety of followup research that not only builds on the mathematical tools developed but also on the insights gained from the project's focus on interpretable and interactive machine learning. As a key application, implementation of the technology obtained from this research on medical images will enable automatic report generation. This technology could reduce the workload of radiologists significantly, and hence reduce human error and optimize resource utilization. Ultimately, reduction in human error or missing cases in radiology can greatly benefit patient well-being and care. More broadly, the work will enhance the ongoing adoption of data-driven thinking in healthcare and thereby help accelerate new discoveries. This project also impacts education, and involves intellectual and professional development of students at a variety of academic stages. Most applications of machine learning to medical imaging (and other human-centric tasks) focus on supervised learning, which demands a large amount of expensive labeled-data. This limitation recurs throughout applications, while real-world use of machine learning demands robustness as well as an ability to work with limited supervision. This project focuses on developing new machine learning tools for working with weak-supervision, while advancing state-of-the-art in interpretable and interactive learning. Moreover, attention to large-scale data and incorporation of domain knowledge is paid, whenever feasible. The project shall also apply the theoretical advances that it will make to a real-world medical imaging application. Theoretical advances of the proposed work will rely on tools from geometry, especially metric learning (including over infinite dimensional spaces), mathematical models motivated by optimal transport theory, as well as nonlinear representations based on neural networks as well as kernel methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.